skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Classical Optimal Control for Energy Minimization Based On Diffeomorphic Modulation under Observable-Response-Preserving Homotopy

Abstract

In this work, we introduce the so-called “Classical Optimal Control Optimization” (COCO) method for global energy minimization based on the implementation of the diffeomorphic modulation under observable-response-preserving homotopy (DMORPH) gradient algorithm. A probe particle with time-dependent mass $m(t;β)$ and dipole $μ(r,t;β)$ is evolved classically on the potential energy surface $V(r)$ coupled to an electric field $E(t;β)$, as described by the time-dependent density of states represented on a grid, or otherwise as a linear combination of Gaussians generated by the k-means clustering algorithm. Control parameters $β$ defining $m(t;β), μ(r,t;β),$ and $E(t;β)$ are optimized by following the gradients of the energy with respect to $β,$ adapting them to steer the particle toward the global minimum energy configuration. We find that the resulting COCO algorithm is capable of resolving near-degenerate states separated by large energy barriers and successfully locates the global minima of golf potentials on flat and rugged surfaces, previously explored for testing quantum annealing methodologies and the quantum optimal control optimization (QuOCO) method. Preliminary results show successful energy minimization of multidimensional Lennard-Jones clusters. Beyond the analysis of energy minimization in the specific model systems investigated, we anticipate COCO should be valuable for solving minimization problems in general, including optimization of parametersmore » in applications to machine learning and molecular structure determination.« less

Authors:
 [1];  [2]; ORCiD logo [2]
  1. Yale Univ., New Haven, CT (United States).
  2. Yale Univ., New Haven, CT (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE; National Science Foundation (NSF)
OSTI Identifier:
1543626
Grant/Contract Number:  
CNS-08-21132; DGE-1144152
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Theory and Computation
Additional Journal Information:
Journal Volume: 14; Journal Issue: 6; Journal ID: ISSN 1549-9618
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Chemistry; Physics

Citation Formats

Soley, Micheline B., Markmann, Andreas, and Batista, Victor S. Classical Optimal Control for Energy Minimization Based On Diffeomorphic Modulation under Observable-Response-Preserving Homotopy. United States: N. p., 2018. Web. doi:10.1021/acs.jctc.8b00124.
Soley, Micheline B., Markmann, Andreas, & Batista, Victor S. Classical Optimal Control for Energy Minimization Based On Diffeomorphic Modulation under Observable-Response-Preserving Homotopy. United States. doi:10.1021/acs.jctc.8b00124.
Soley, Micheline B., Markmann, Andreas, and Batista, Victor S. Fri . "Classical Optimal Control for Energy Minimization Based On Diffeomorphic Modulation under Observable-Response-Preserving Homotopy". United States. doi:10.1021/acs.jctc.8b00124. https://www.osti.gov/servlets/purl/1543626.
@article{osti_1543626,
title = {Classical Optimal Control for Energy Minimization Based On Diffeomorphic Modulation under Observable-Response-Preserving Homotopy},
author = {Soley, Micheline B. and Markmann, Andreas and Batista, Victor S.},
abstractNote = {In this work, we introduce the so-called “Classical Optimal Control Optimization” (COCO) method for global energy minimization based on the implementation of the diffeomorphic modulation under observable-response-preserving homotopy (DMORPH) gradient algorithm. A probe particle with time-dependent mass $m(t;β)$ and dipole $μ(r,t;β)$ is evolved classically on the potential energy surface $V(r)$ coupled to an electric field $E(t;β)$, as described by the time-dependent density of states represented on a grid, or otherwise as a linear combination of Gaussians generated by the k-means clustering algorithm. Control parameters $β$ defining $m(t;β), μ(r,t;β),$ and $E(t;β)$ are optimized by following the gradients of the energy with respect to $β,$ adapting them to steer the particle toward the global minimum energy configuration. We find that the resulting COCO algorithm is capable of resolving near-degenerate states separated by large energy barriers and successfully locates the global minima of golf potentials on flat and rugged surfaces, previously explored for testing quantum annealing methodologies and the quantum optimal control optimization (QuOCO) method. Preliminary results show successful energy minimization of multidimensional Lennard-Jones clusters. Beyond the analysis of energy minimization in the specific model systems investigated, we anticipate COCO should be valuable for solving minimization problems in general, including optimization of parameters in applications to machine learning and molecular structure determination.},
doi = {10.1021/acs.jctc.8b00124},
journal = {Journal of Chemical Theory and Computation},
number = 6,
volume = 14,
place = {United States},
year = {2018},
month = {4}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

Save / Share: